1. Field of the Invention
The present invention relates generally to photoplethysmography (“PPG”) and, more particularly, to a method and apparatus for extraction of respiratory rate from pulse oximetry data for patient care.
2. Background of the Related Art
For patients at risk of cardio-respiratory failure, it is important to monitor the efficiency of gas exchange in the lungs, that is, the oxygenation of arterial blood flow. Pulse oximetry provides a non-invasive means to monitor arterial oxygen saturation (SaO2) on a continuous basis, based on photoplethysmography techniques used in patient monitors during anesthesia and in intensive care units. For example, see U.S. Pat. No. 7,169,110 to Lee, et al., the contents of which are incorporated herein by reference.
Pulse oximeters can be used to measure both SaO2 and basic cardiac function (e.g., heart rhythms). In addition to being simple to operate, pulse oximeters are non-invasive and do not create any discernable patient discomfort. Respiratory rate is important for many clinical uses, including prevention of sleep apnea, sudden infant death syndrome (SIDS) and chronic obstructive pulmonary disease. Patient respiratory rate, even the respiratory rate of an infant, can be extracted from pulse oximetry, as the pulse oximeter signal includes both heart rate and respiratory signal data.
Present practice for automatic respiration rate measurement requires monitoring of CO2 production using a capnograph. However, the capnograph is an expensive device that requires a significant amount of maintenance. In addition, the capnograph requires a mask or nasal cannula, and is therefore obtrusive to the patient and cumbersome to use. Accordingly, there is a need for a less intrusive method for obtaining accurate respiratory rates, such as by use of pulse oximeters, in addition to SaO2 data.
In the present invention, respiratory rate is obtained by detecting the presence of baseline, amplitude and frequency modulations. However, prior efforts in this field have found it difficult to detection the modulations, due to myriad causes. Three primary culprits stand out: the time-varying nature of these modulations; the often subtle nature of both amplitude and frequency modulations, thus creating a need for a highest possible time and frequency resolution for detection; and masking by motion and noise artifacts of amplitude and frequency modulations. Also see discussion of shortcomings outlined by Nakajima, et al., Monitoring of Heart and Respiratory Rates by Photoplethysmography Using a Digital Filtering Technique, Med. Eng. Phy. Vol. 18, No. 5, pp. 365-372 (1996). The present invention overcomes the difficulty encountered by conventional systems, including the system suggested by Nakajima, et al., in obtaining data regarding respiratory rate and arterial blood flow oxygenation.
Past and on-going research efforts have analyzed Time-Varying (TV) signals and Short Time Fourier Transform (STFT) algorithms in an effort to obtain a simple to implement solution. However TV signals and STFT algorithms cannot provide simultaneous high time-frequency resolution.
A Wigner-Ville distribution approach, which is one of the Cohen class time-frequency spectral methods, can provide one of the highest time and frequency resolutions. However, the Wigner-Ville distribution approach is limited in the creation of artificial cross terms in the case of signals with multi-frequency components. Efforts to curtail undesired cross terms with the Wigner-Ville distribution have resulted in many different techniques, all based on utilizing either or both time and frequency windows. The consequence of using either time or frequency domain is a degradation of resolution in the other domain (frequency or time, respectively). That is, the aforementioned methods fix both time and frequency resolutions.
A recently introduced Time-Frequency (TF) spectral method, Hilbert-Huang Transform (HHT), provides both high time and frequency resolutions. The HHT is based on combinational use of empirical mode decomposition and Hilbert transform. Because the HHT does not rely on the use of the Fourier transform, concomitant high TF resolution can be obtained. However, capability of the HHT degrades in tandem with increasing levels of noise contamination.
The above-described conventional methods are known as nonparametric approaches, since they do not characterize data into a model where a few sets of parameters are used to capture essential features of the data. Most nonparametric approaches require sufficiently long data record lengths.
Parametric methods, in contrast, are useful for analyzing short data records and provide concomitant high TF resolution without any unwanted cross terms in multicomponent signals. Some autoregressive model-based TF spectral methods include the recursive least squares, least mean squares, and Kalman filter. These methods can adaptively track slowly TV dynamics, which are represented by a few parameters from which Time-Frequency Spectra (TFS) can be obtained. However, these methods are limited in that they are more suitable for slow TV signals and are sensitive to the choice of the number of model coefficients.
Accordingly, a Time-Varying Optimal Parameter Search (TVOPS) has been developed to alleviate sensitivity to model order choice and to provide high time-frequency resolution even for short data records. A limitation exists, however, as it does not preserve amplitudes of TFS, as is the case with all parametric TFS methods.
While Complex DeModulation (CDM), which does preserve instantaneous amplitudes, has been previously used for amplitude-modulating signals and applied to instantaneous frequency estimation, its application to TFS has not yet been fully explored. CDM assumes that only a single frequency is present within a predefined frequency band that may not be arbitrarily small, which is a limitation. Consequently, the TF resolution is not optimal. To overcome this limitation, a version of CDM has been developed in the present invention that uses variable frequencies, providing high time-frequency resolution as well as preservation of the amplitudes of TFS. This aim is motivated by the fact that no single algorithm is able to provide concomitant high time-frequency resolution as well as preservation of the amplitude distribution of the signal. This approach has two steps, in which the TVOPS is utilized to obtain TFS and then the Variable Frequency Complex DeModulation (VFCDM) is used to obtain even more accurate TFS and amplitudes of the TFS. The inventive combination of the TVOPS and VFCDM provides higher TF resolution than most other TFS approaches, in addition to preserving amplitude distributions of the TF spectra.
Recent efforts that use advanced signal processing algorithms in an attempt to overcome the aforementioned problems have used a series of adaptive Low Pass Filters (LPF) followed by High Pass Filters (HPF) with suitable cut-off frequencies, as described by Nakajima et al. These efforts, however, are able to distinguish heart and respiratory signals in the PPG signal, and accuracy degrades with motion artifacts, which are especially prevalent in the PPG signal during exercise. Furthermore, the cutoff frequencies of the LPF and HPF must be individually tailored, precluding wide clinical use.
New techniques that estimate time-frequency spectra for analyzing non-stationary signals utilize STFT and a Continuous Wavelet Transform (CWT) to extract respiratory rate from the PPG signals. However, success of these techniques is predicated on obtaining the highest possible time and frequency resolution, which is not possible with either the STFT or the CWT. It is widely known that the CWT cannot provide concomitant high time and frequency resolution as it only provides high frequency resolution at low frequencies and high time resolution at high frequencies.
For subjects with chronic obstructive pulmonary disease, reflection of respiratory rate via the amplitude and frequency modulations of a PPG signal is often subtle, as physical limitations often preclude these subjects from breathing in a normal manner. It is unclear what is considered “low frequency” because the low frequency range can vary depending on the dynamics of the system. Furthermore, real-time implementation is especially challenging for the CWT. Despite recent advances to improving accuracy of PPG signals and advanced signal processing algorithms, a method does not exist that allows an apparatus to accurately determine respiratory rate from pulse oximeter data.
Accordingly, the present invention overcomes limitations of conventional systems by applying a new algorithm that accurately extracts continuous respiratory rate from noninvasive recordings of PPG signals. The algorithm utilizes a highest possible time and frequency resolution approach to estimate TFS and associated amplitudes via use of VFCDM, which provides the highest time and frequency resolution and most accurate amplitude estimates as compared to smoothed pseudo Wigner-Ville, continuous wavelet transform and Hilbert-Huang transform methods. Thus, the VFCDM algorithm is significantly more accurate than the power spectral density, CWT and other conventional time-frequency based methods for determining respiratory rate.